最大化算術(shù)-幾何均值距離的多傳感器遙感圖像配準(zhǔn)

時(shí)永剛

最大化算術(shù)-幾何均值距離的多傳感器遙感圖像配準(zhǔn)

時(shí)永剛

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出版歷程
- 收稿日期: 2004-11-23
- 修回日期: 2005-06-07
- 刊出日期: 2006-04-19

Multi-sensor Remote Sensing Image Registration by Maximization of Arithmetic-Geometric Mean Divergence

Shi Yong-gang

摘要

摘要: 互信息是多模態(tài)醫(yī)學(xué)圖像配準(zhǔn)的一種重要方法。它測(cè)量的是兩個(gè)概率分布之間的Kullback-Leibler(KL)距離。該文分析了KL距離和Shannon不等式之間的關(guān)系，在此基礎(chǔ)上，提出了一種新的算術(shù)-幾何均值距離，并將這一距離測(cè)度用于多傳感器遙感圖像的配準(zhǔn)處理。與Kullback-Leibler距離不同，新的距離測(cè)度具有對(duì)稱性，并且對(duì)概率值為0的情況不需要特殊處理。文中首先通過(guò)一維仿真信號(hào)對(duì)算術(shù)-幾何(AG)測(cè)度進(jìn)行了分析，并使用Thematic Mapper (TM), Satellite POsitioning and Tracking (SPOT)遙感圖像和雷達(dá)圖像進(jìn)行了配準(zhǔn)實(shí)驗(yàn)，驗(yàn)證了提出的新的算術(shù)-幾何均值距離函數(shù)在配準(zhǔn)多傳感器遙感圖像方面的有效性。與目前常用的相關(guān)系數(shù)的方法不同，這種新方法對(duì)于像素灰度值不具有線性關(guān)系的多傳感器遙感圖像能夠?qū)崿F(xiàn)配準(zhǔn)處理。
- 圖像配準(zhǔn);多傳感器遙感圖像;配準(zhǔn)測(cè)度;算術(shù)-幾何均值距離;Kullback-Leibler距離
Abstract: Mutual information is an important method for multimodal medical image registration. It measures Kullback-Leibler (KL) divergence between two probability distributions. The connection between KL divergence and Shannon inequality is investigated. Base on the connection, a novel measure, Arithmetic-Geometric (AG) mean divergence, is proposed. It can be used for alignment of remote sensing images acquired by different sensors. Unlike KL divergence, the new measure is symmetry and do not require the condition of absolute continuity to be satisfied by the probability distribution involved. AG divergence measure is applied to one-dimensional simulated signals, and to affine registration of Thematic Mapper (TM), Satellite POsitioning and Tracking (SPOT) and Synthetic Aperture Radar (SAR) remote sensing images. The performance of AG divergence measure is validated by experiments. The results show that AG measure do not require the approximate linear relation of pixel intensity value in image pairs, and is practicable even though the gray values of images are much different from each other.

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參考文獻(xiàn)(1)

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